Regularization Path Algorithms for Detecting Gene Interactions

In this study, we consider several regularization path algorithms with grouped variable selection for modeling gene-interactions. When tting with categorical factors, including the genotype measurements, we often dene a set of dummy variables that represent a single factor/interaction of factors. Yuan & Lin (2006) proposed the groupLars and the group-Lasso methods through which these groups of indicators can be selected simultaneously. Here we introduce another version of group-Lars. In addition, we propose a path-following algorithm for the group-Lasso method applied to generalized linear models. We then use all these path algorithms, which select the grouped variables in a smooth way, to identify gene-interactions aecting disease status in an example. We further compare their performances to that of L2 penalized logistic regression with forward stepwise variable selection discussed in Park & Hastie (2006b).